Particle swarm optimization application in optimization

Abstract

The Particle Swarm Optimization (PSO) was used to select the three best inputs to explain the input-output
relationship of both 'defects' and 'time' models. A ranking-based system was used to select the best features. Using this system, the value of each particle in the swarm represents the importance of each feature. During optimization, the three best-ranked features were used to train the Multilayer Perceptron (MLP). The objective of the PSO is to minimize the MSE fitting error between the actual output and the modelled output. If the features are discriminative, the generalization error should be small since the MLP approximation is close to the actual output.